JP2008083971A - Method for simulating system having solar generator/wind generator/cogenerator - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a user-friendly simulation method and simulation software that can compute such a combination in a system for supplying more electrical and thermal output energy, minimizes initial costs, minimizing running costs and minimizing CO<SB>2</SB>emissions, by weather and demand estimation and forecasting. <P>SOLUTION: The software for simulating each apparatus scale/structure and operation method in a system, using a wind generator, a photovoltaic generator, a solar thermal collector (device for producing hot water etc.) and a cogenerator independently, combinedly, or in a hybrid manner (for a hybrid device), comprises one or more independent or combined software modules of a weather forecasting software module, a power/heat demand forecasting software module and a system configuration/operation method optimization software module. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

The present invention relates to the field of simulation methods and simulation software. More particularly, it relates to the field of electrical simulation and simulation software for electrical and thermal output energy, equipment installation costs, operating costs, and CO ₂ emissions in systems supplying electrical and thermal energy.

Single use of wind power generators, solar power generators, solar heat collectors, and cogeneration devices for consumer, commercial, and educational facilities for the purpose of reducing costs, energy consumption, or carbon dioxide (CO ₂ ) emissions There are many introduction plans / simulation examples (for example, Non-Patent Documents 1, 2, and 3) for systems that are used together or combined (in the case of hybrid devices), and there are different installation targets for each facility. Using independent simulation methods and conditions, electrical and thermal output energy, equipment installation cost, operating cost, CO ₂ emission, etc. when the system is introduced are calculated.

For example, in Non-Patent Documents 4, 5, and 6, electrical and thermal output energy, equipment installation cost, operating cost, CO ₂ when the system is introduced in a facility where power and heat demand data are measured in advance. Emissions are calculated.

  Regarding the introduction of cogeneration equipment, the program “CASCADE” for evaluating the effects of cogeneration introduction introduced in Non-Patent Document 7 includes (1) Load data for 5 buildings including hotels and hospitals. (2) Can synthesize load data and can be used for multi-use buildings. (3) Each system of gas engine, gas turbine, and fuel cell can be evaluated. (4) One year (12 months) Calculate energy every hour on the representative day of the month. (5) For operation, both power load tracking and thermal load tracking modes can be evaluated. (6) Power purchase control amount, generator load factor, operating time Can set detailed conditions such as use of exhaust heat, (7) Can evaluate a system incorporating a hot water tank, (8) Can also evaluate reverse power flow when surplus power is sold, 9) energy saving, economical, environmental resistance could be evaluated, it has a characteristic that has been standardized (10) as a graphical output of the calculation result menu.

  In Non-Patent Document 8, in facilities where power and heat demand data has not been measured in advance, the power and heat demand for each month / business holiday is entered by inputting information such as the type of business using the power and / or heat demand model pattern. There has been proposed a program having a function for automatically creating a pattern and a function for automatically capturing measurement data of a power demand pattern when it is electronically available. In addition, it is possible to optimize a system including a storage battery, a cogeneration system, a boiler, a refrigerator, and a heat pump. (1) Cogeneration equipment: gas turbines, gas engines, and diesel engines, (2) boilers: steam boilers and hot water boilers, (3) refrigerators: electric, steam absorption, and direct absorption, 4) Heat pump: both hot and cold. Moreover, the simulation about the apparatus which can select an electric main and a heat main as an operation mode of a cogeneration apparatus is possible.

Non-Patent Document 9 proposes a system that controls a microgrid including a fuel cell, a storage battery, and a hot water tank, assuming an apartment house or a detached house. This control system has functions such as demand forecasting and supply and demand planning. In the demand prediction, the past power / heat demand record of each house is analyzed, and the demand in 30 minutes over 24 hours according to the weather forecast of the next day taken from outside is predicted. In the supply and demand plan, the start / stop time of the power source (fuel cell) and the storage battery charge / discharge amount are set to 1 a day so as to minimize the CO ₂ emission amount of the entire site (here, the house) with respect to the demand forecast. Plan times.

Non-Patent Document 10 proposes a system for drafting an optimum plan for electric power supply and demand for one week in the future every 30 minutes for electric heat demand in an existing microgrid. This system obtains electric power demand prediction, natural energy output prediction, and generation amount of sewage sludge digestion gas as an energy source from past results and weather forecast information as input conditions for optimization of supply and demand operation plan. The method is adopted.

Mariko Kikata, Yasuyuki Endo, Akito Ito, “Evaluation of CO2 Emissions from Electricity and Heat Supply System Based on Energy Flow”, IEEJ Transactions B, Vol. 124, no. 1, pp. 53-61, 2004 Akio Tanaka, Naoto Sagawa, Chiharu Murakoshi, Hidetoshi Nakagami, “Examination of Energy Efficiency Measurement and Efficiency Improvement of Commercial Building Heat Source Facilities”, Proceedings of the 20th Energy System, Economic and Environmental Conference (2004. 1.29 to 30), pp. 267-270, 2004 Yuji Sugimoto, Masahiro Kobayashi, Takashi Yanagihara, Takashi Yatabe, “Practice and Evaluation of Energy Saving in Large-scale Hotels”, 20th Energy System / Economic / Environmental Conference Conference Proceedings (2004.1.29-30), pp. 115-118, 2004 Takeyoshi Kato, Tetsuhisa Iida, Kure Kai, Yasuo Suzuoki, “A Study on the Construction of Residential Micro-Cogeneration Considering Fluctuation of Hot Water Supply Load Based on Measurements”, IEEJ Transactions B, Vol. 121, no. 12, pp. 1748-1755, 2001 Shinichiro Kaji, Kishige Yoshimi, Hiroshi Takikawa, Kenki Sugawara, “Reduction of Life Cycle CO2 Emissions by Introducing Solar / Heat / Cogeneration System to Consumer Facilities”, IEEJ Transactions B, Vol. 123, no. 11, pp. 1365-1372, 2003 Shinichiro Minato, Kishige Kaji, Hiroshi Takikawa, Kenki Sugawara, “Effects of System Operation on CO2 Emissions from Solar / Heat / Cogeneration Systems”, IEEJ Transactions B, Vol. 125, no. 10, pp. 930-938, 2005 Edited by the Japan Institute of Energy, “Natural Gas Cogeneration Planning and Design Manual 2002”, p. 45, Nippon Kogyo Publishing, 2002 Satoshi Adachi, Shuichi Umezawa, “Development of Consumer Energy System Optimization Program”, 2006 IEEJ Power and Energy Division Conference Proceedings CD-ROM, pp. 38-27-38-28, 2006 Takaya Yamamoto, Yoshiyuki Takuma, Mao Inoue, Moto Arao, “Examination of energy supply for houses using microgrids”, Proceedings of 2006 Annual Conference of the Institute of Electrical and Electronics Engineers of Japan, CD-ROM, pp. 8-21-8-22, 2006 Masahiro Koshio, Yasuhiro Kojima, "Examination by Micro Grid Demonstration (Part 2)-Supply and Demand Operation Plan-", Proceedings of 2006 Annual Conference of the Institute of Electrical Engineers of Japan, CD-ROM, pp. 9-11-9-12, 2006

  However, when calculating the simulation of the introduction of the system to a specific facility in detail, unless a cogeneration system is used and a power and heat supply system is assumed, use a separately created calculation program. Or rely on very simple approximation calculations.

  In addition, when simulating the operation of the system retroactively, it is necessary to use past accumulated measurement demand data or use a power and heat demand model pattern prepared in advance. In many cases, the power and heat demand model pattern prepared in advance is a record of hourly power and heat demand on the representative day of each month of one year (12 months), and does not take into account differences in weather conditions .

  Further, when simulating the operation of the system retroactively, the assumed operation method is limited to power load tracking and heat load tracking, and a more flexible and highly efficient operation method is not implemented in the simulator.

  In addition, the software shown in Non-Patent Documents 9 and 10 for planning the operation method of the cogeneration device and the power supply device using the weather forecast and the demand prediction is applied only to a specific system configuration designated by the software producer. And cannot be used in facilities where the system is already installed.

  In addition, since the places where past weather data are accumulated and the places where weather forecasts are published are limited, when simulating the system at any point throughout the country, the past is quite different from the actual Weather data and weather forecasts will be used.

The present invention has been made in order to solve the above-mentioned problems, and the object of the present invention is to provide a facility in advance in a facility where the system is to be introduced or a facility in which the system is introduced. Even if past accumulated meteorological data and / or past accumulated demand data of facilities are not accumulated, simulation using past estimated weather data and / or past estimated demand data, past estimated weather data and / or past estimated demand Obtain as much electrical and thermal output energy as possible through simulation using data and weather forecasts and demand forecasts, reduce equipment installation costs as much as possible, reduce operating costs as much as possible, and CO ₂ emissions as much as possible Get a combination that reduces (best matching It is an object of the present invention to provide a user-friendly simulation method and simulation software.

  The present invention relates to a device scale configuration of a system that uses a wind power generator, a solar power generator, a solar heat collecting device (manufacturing device such as hot water) and a cogeneration device for single use, combined use, or combination (in the case of a hybrid device), and Software for simulating operation methods, consisting of one or more independent or integrated software modules among a weather prediction software module, a power / heat demand prediction software module, and a system configuration / operation method optimization software module It is characterized by being.

  In the simulation method and simulation software of the present invention, an optimal system construction mode for outputting a system configuration optimized or optimized for a facility to which the system is to be introduced, and a facility for which the system is introduced are preferable. It is possible to select an optimum operation command mode that outputs a system operation method that has been converted into a system.

  In the facility where the system is to be introduced, the system configuration / operation method optimizing software module goes back to the past to perform a system operation simulation including a wind power generator, a solar power generator, and / or a solar heat collector. In order to do so, the past accumulated weather data of the point where the facility exists is necessary. However, such data is rarely prepared. The meteorological prediction software module uses an arithmetic unit to empirically estimate past estimated weather data at an unobserved point using available past measured weather data and store it in a storage device.

  In addition, in the facility where the system is introduced, when the system operation method from 1 hour to 24 hours (from 1 hour to 24 hours) is optimized from the present to 24 hours ahead, By predicting future predicted supplyable energy calculated using an arithmetic unit, a predicted use operation optimization function is realized.

  In addition, the weather prediction software module can be used to display any forecasted weather change predicted using a computing device on the screen of a computer or TV, so that the forecasted weather change can be used in daily life or business. It has a forecast weather change graph display function that displays it in a graph format.

  In the facility where the system is to be introduced, when the system configuration / operation method optimizing software module traces the operation of the system retroactively, the past accumulated measurement demand data is required. However, such data is rarely prepared. This software uses the power / heat demand prediction software module to apply past combinations of power and / or heat demand model patterns prepared in advance to past estimated demand data from estimated weather data. Therefore, it is possible to perform a realistic simulation in which electric power and heat demand fluctuate daily according to weather conditions.

  The facility to be simulated is a hotel, a hospital, a large commercial facility, a university, a collective dwelling, a factory, or a combination thereof.

In addition, the system configuration / operation method optimization software module calculates the operation cost based on the power charge system / gas charge system and the inherent constraints in the facility to be simulated, as well as the CO for each energy source. _It has a function to calculate CO ₂ emissions and primary energy consumption based on ₂ emissions intensity and primary energy consumption intensity.

  Further, the constraint conditions are the site area of the facility that is the simulation target, the total floor area, the area where new equipment can be constructed, the presence or absence of thermal piping work, the presence or absence of wind power generators, the presence or absence of solar power generators, It is the presence or absence of introduction of a solar heat collector, the presence or absence of introduction of a cogeneration device, the presence or absence of introduction of a heat storage tank, and the upper limit or lower limit of the installed capacity and quantity of those devices / equipment.

The simulation software and the simulation method of the present invention are included in the system in which the wind power generator, the solar power generator, the solar heat collector, and the cogeneration device are used singly, in combination, or combined (in the case of a hybrid device). Electrical and thermal output energy, equipment installation costs, operating costs, CO ₂ emissions by simulating equipment size, configuration, quantity and operating method using forecasted or estimated weather data and power and heat demand data The amount can be best matched more effectively than the prior art.

  In addition, when the operation simulation of the system is performed retroactively, in order to assume the predictive operation optimization function, the power load following operation and the thermal load following operation described in the background art [0004] and [0005] are assumed. Highly efficient and realistic driving simulation is possible.

  In addition, the background technologies [0004] and [0005] simulate the operation of the cogeneration apparatus using the hourly power and heat demand patterns on the monthly representative days of one year (12 months). However, in this software, the past estimated demand data according to claim 5 is converted from the past estimated weather data according to claim 2 by the electric power / heat demand prediction software module according to claims 1, 5, and 6. Since the estimation is made by applying some power and / or heat demand model patterns prepared in advance, a realistic simulation in which the power and heat demands fluctuate daily according to weather conditions is possible.

  The background art [0006] and [0007] can be applied only to a specific power and / or thermal energy supply system. In the present software, the system configuration according to claim 1, 7, and 8 is used. -Gas engine, gas turbine, diesel engine, fuel cell, storage battery, gas boiler, heavy oil boiler, exhaust heat recovery boiler, electric heat pump, gas heat pump, exhaust heat input type heat pump, heat storage tank, built-in operation method optimization module The present invention can be applied to any system as long as the system is a combination of a solar power generation device, a solar heat collection device, and a wind power generation device.

  In addition, the background technologies [0006] and [0007] do not assume demand forecasts and supply-demand plans at unobserved points where past accumulated weather data cannot be obtained or non-forecast points where weather forecasts cannot be obtained. However, this software can predict past forecast weather data at any point in Japan and forecast future weather changes with the weather forecast software module, so that more realistic and accurate demand forecast can be predicted. Is possible.

  As shown in FIG. 1, the simulation software and simulation method of the present invention are composed of three modules: a weather prediction software module, a demand prediction software module, and a system configuration / operation method optimization software module.

  The operation of the software module group in the optimum system construction mode is shown in FIG. First, the user specifies the type / point of the facility to be simulated, and sets the constraint conditions specific to the facility. Next, the weather prediction software module uses the arithmetic device to estimate past estimated weather data at a point where the facility exists. Next, when the historical measurement demand data is not accumulated in the facility, the demand estimation prediction software module uses the arithmetic device and uses the past estimation weather data, power and / or heat demand model pattern, Past estimated demand data is estimated and stored in a storage device. Next, the system configuration / operation method optimizing software module uses an arithmetic unit, reads these data from the storage device, simulates the operation of the system, and outputs the system configuration according to the purpose designated by the user.

  FIG. 3 shows the operation of the software module group in the optimum operation command mode. First, the user designates the type and location of a facility to be used for simulation, and sets a constraint condition specific to the facility. Next, the weather prediction software module predicts a future predicted weather change at a point where the facility exists from the arithmetic unit. Next, the demand estimation prediction software module predicts a future predicted demand change of the facility from the arithmetic unit. Next, the system configuration / operation method optimizing software module simulates the operation of the system using an arithmetic device using the predicted weather change and the predicted demand change in the future, and outputs the operation method according to the purpose specified by the user To do.

  The weather prediction algorithm in the weather prediction software module includes an auto-regressive model, a moving average model, an auto-regressive moving average model, and a non-uniform auto-regressive condition. heteroscale model, auto-regressive integrated moving average model, multiple regression model, multi-layered neural network, recurrent neural network, recurrent neural network one of neural networks, radial basis function network, Hopfield model, genetic algorithm, genetic programming, nearest neighbor method, nth order function, and mean value, or any of these You may use the algorithm which combined 2 or more, or the other algorithm published. In addition, the weather estimation algorithm includes an auto-regressive model, a moving average model, an auto-regressive moving average model, and a non-uniform auto-regressive conditioned model. Auto-regressive integrated moving average model, multi-regressive neural network, multi-layered neural network, recurrent neural network (recurrent neural network) s), radial basis function network, hopfield model, genetic algorithm, genetic programming, nearest neighbor method, n-order function, and mean value, or these two A combination of two or more algorithms or other publicly available algorithms may be used.

  The demand prediction algorithm in the demand prediction software module includes an auto-regressive model, a moving average model, an auto-regressive moving average model, and a non-uniform auto-regressive condition. heteroscale model, auto-regressive integrated moving average model, multiple regression model, multi-layered neural network, recurrent neural network, recurrent neural network one of neural networks, radial basis function network, Hopfield model, genetic algorithm, genetic programming, nearest neighbor method, nth order function, and mean value, or any of these You may use the algorithm which combined 2 or more, or the other algorithm published. In addition, the demand estimation algorithm includes an auto-regressive model, a moving average model, an auto-regressive moving average model, and a non-uniform auto-regressive constant model. Auto-regressive integrated moving average model, multi-regressive neural network, multi-layered neural network, recurrent neural network (recurrent neural network) s), radial basis function network, hopfield model, genetic algorithm, genetic programming, nearest neighbor method, n-order function, and mean value, or these two A combination of two or more algorithms or other publicly available algorithms may be used.

  In addition, the forecast weather change is highly useful information that can be used in addition to the simulation of the system, so the forecast weather change graph display function can be used to display any weather specified by the user on the screen of a PC or TV. Display in graph format.

  In both the optimum system construction mode and the optimum operation command mode, the user sets various conditions on a graphical user interface displayed on the screen of a display device such as a personal computer or a television.

  When specifying the point where the facility to be calculated exists, click the mouse on an arbitrary position on the map type interface displayed on the screen of the display device or input latitude / longitude information .

  Further, the map type interface is color-coded on the map according to past estimated weather data calculated by the weather prediction software module and the accuracy of prediction and prediction of future predicted weather changes.

  In the optimum operation command mode, as shown in FIG. 4, an optimum operation of the system is automatically performed by inputting an operation command signal from a personal computer in which the software is installed to the control device of the system. Can be ordered.

  FIG. 5 shows an example of a weather prediction result using the neural network, genetic algorithm, and continuous prediction implemented in the weather prediction software module of this software. As shown in the figure, the prediction error when the neural network is used is the smallest regardless of the season.

  FIG. 6 and FIG. 7 show examples of operation pattern improvement by the optimum operation command in the case of a system that uses a solar power generation device, a solar heat collection device, a heat storage tank, and a cogeneration device. FIG. 6 shows a case where the cogeneration apparatus (gas engine) is operated by heat main power. FIG. 7 shows a case where operation is performed so as to minimize the metered charges of power and gas, based on the operation method implemented in the software configuration and software method optimization software module of this software. In the heat main power operation shown in FIG. 6, even when a sufficient heat supply is obtained from the solar heat collector, the cogeneration device (gas engine) is operated in accordance with the heat demand, so a large amount of surplus heat is generated. However, in the operation that minimizes the metered charge of power and gas shown in FIG. 7, the heat supply from the solar heat collector and the heat storage tank and the heat supply from the cogeneration device (gas engine) match the heat demand. Little surplus heat is generated.

Consumer, commercial and educational facilities are required to save energy, save costs and reduce CO ₂ emissions.

  Promising measures to achieve this reduction are systems that use wind generators, solar power generators, solar heat collectors, and cogeneration devices for single use, combined use, or combined use (in the case of hybrid devices). .

The simulation method and simulation software according to the present invention provide a system configuration in which the cost, energy consumption, and CO ₂ emission amount are best matched when the system is to be introduced into the facility. Even if there is no data, it can be output.

In addition, the simulation method and simulation software of the present invention provide an operation method that best matches the cost, energy consumption, and CO ₂ emissions for the facility in which the system is introduced. Output using demand change.

  The simulation method and simulation software of the present invention, which was not easy until now and can simply simulate introduction of the system in the facility, have sufficient industrial applicability.

It is a block diagram which shows the structure of the simulation method and simulation software of this invention. It is a block diagram which shows operation | movement of each software module group in optimal system construction mode. It is a block diagram which shows operation | movement of each software module group in optimal driving | operation command mode. It is a block diagram which shows the mode of the system control by the output of a driving command signal in the optimal driving command mode. It is an example of weather prediction results using a neural network and a genetic algorithm implemented in the weather prediction software module. It is an example of an operation pattern in the case of a heat main power slave operation in a system that uses a solar power generation device, a solar heat collecting device, and a cogeneration device. It is an example of an operation pattern in the case of the operation method which minimizes the metered charge of electric power and gas in the system which uses a solar power generation device, a solar heat collecting device, and a cogeneration apparatus.

Explanation of symbols

1: Electric power demand 2: Power supplied from cogeneration equipment (gas engine) 3: Power supplied from photovoltaic power generation equipment 4: Received power (electric power purchased)
5: Electricity sales power 6: Heat demand 7: Heat supplied from solar heat collector and heat storage tank 8: Heat supplied to heat storage tank 9: Waste heat 10: Heat supplied from cogeneration system (gas engine)

Claims (10)

One of the weather prediction software module, the power / heat demand prediction software module, and the system configuration / operation method optimization software module constructed by software on the computer, either independently or by merging two or more. A method of simulating an energy system and operation using a wind power generator, a solar power generator, a solar heat collector, and a cogeneration device. 2. The past estimated weather data calculating function for empirically estimating past weather at a point where weather observation has not been performed in the past and present from past accumulated weather data in the weather prediction software module. Simulation method. In the weather prediction / prediction software module according to claim 1, past accumulated weather data, past estimated weather data of past and present unobserved points calculated by the simulation method according to claim 2, current weather data, and current The simulation according to claim 1, further comprising a future forecast weather change calculation function that predicts the weather at any prediction target point as a change over time from only one or more than two weather forecast data. Method. 3. The weather prediction software module according to claim 2, wherein useful point data is automatically selected and extracted from past accumulated weather data based on latitude and longitude, and past estimated weather of past and present unobserved points. The simulation method according to claim 1, further comprising a function of calculating data empirically. In the weather prediction software module according to claim 3, past accumulated weather data, past estimated weather data of past and present unobserved points calculated by the simulation method according to claim 2, current weather data, and current Automatically select and extract useful point data based on latitude and longitude from only one or more than two weather forecast data, and forecast future weather changes at any forecast target point The simulation method according to claim 1, wherein the simulation method has an empirical calculation function. The power / heat demand prediction module according to claim 1, wherein the past power and heat demand data for a target facility for which past and present power and heat demand measurement data are not accumulated are prepared in advance. 5. A past estimated demand data calculation function that empirically estimates from past estimated weather data estimated by the weather prediction software module according to claim 2 using a demand model pattern. The simulation method described in 1. The power / heat demand prediction module according to claim 1, the past estimated weather data according to claim 2, the future predicted weather change according to claim 5, and the power and heat according to claim 6. Past power for a target facility that does not store past and present power and heat demand measurement data from one or more of the demand model pattern, past accumulated measurement demand data, and past estimated demand data The simulation method according to claim 1, further comprising a function of calculating a predicted demand change in the future for predicting heat demand as a change over time. The system configuration / operation method optimizing module according to claim 1, wherein the electric power and heat in the energy system in which the wind power generator, the solar power generator, the solar heat collecting device, and the cogeneration device are used singly, in combination, or combined. The simulation method according to claim 1, wherein the system configuration and the operation method for best matching the dynamic output energy, device installation cost, operation cost, and CO ₂ emission are output. The system configuration / operation method optimizing module according to claim 1, wherein a future predicted meteorological change calculated by the weather prediction module according to claim 2 to 4 is used to calculate an arbitrary scale at the same point. A future predictable energy change calculation function for predicting the amount of electric power and heat energy obtained when installing the wind power generator, the solar power generator, and the solar heat collecting apparatus as a change with time. The simulation method described in 1. In the system configuration / operation method optimizing module according to claim 1, a future predicted supplyable energy change predicted by a future predicted supplyable energy change calculation function according to claim 9 and a future predicted demand according to claim 7. By using the forecasted demand change predicted by the change calculation function, the operation method of the energy system that uses wind power generators, solar power generators, solar thermal collectors and cogeneration devices for single use, combined use, or joint use is determined hourly. The simulation method according to claim 1, further comprising a predicted use operation optimizing function that optimizes operation cost or CO ₂ emission amount.

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